Nonetheless, the training of high-quality generative models demands high-performance computing platforms,
putting the process out of reach for most users.
Furthermore, training a high-quality model requires expensive large-scale data collection and careful pre-processing. Commonly used datasets such as ImageNet [1]} and LSUN [2]} require human annotation and manual filtering. The specialized FFHQ Face dataset [3]} requires delicate face alignment and super-resolution pre-processing. Moreover, the technical effort is not trivial: developing an advanced generative model requires the domain knowledge [4]}, [5]} of a team of experts, who often invest months or years into a single model on specific datasets.
